skills/analyzing-indicators-of-compromise/SKILL.md
Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with multi-source intelligence; or making block/monitor/whitelist decisions. Activates for requests involving VirusTotal, AbuseIPDB, MalwareBazaar, MISP, or IOC enrichment pipelines.
npx skillsauth add mukul975/cyber-skills analyzing-indicators-of-compromiseInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when:
Do not use this skill in isolation for high-stakes blocking decisions — always combine automated enrichment with analyst judgment, especially for shared infrastructure (CDNs, cloud providers).
requests and vt-py libraries, or SOAR platform with pre-built connectorsBefore enriching, classify each IOC:
evil[.]com), extract registered domain via tldextractDefang IOCs in documentation (replace . with [.] and :// with [://]) to prevent accidental clicks.
VirusTotal (file hash, URL, IP, domain):
import vt
client = vt.Client("YOUR_VT_API_KEY")
# File hash lookup
file_obj = client.get_object(f"/files/{sha256_hash}")
detections = file_obj.last_analysis_stats
print(f"Malicious: {detections['malicious']}/{sum(detections.values())}")
# Domain analysis
domain_obj = client.get_object(f"/domains/{domain}")
print(domain_obj.last_analysis_stats)
print(domain_obj.reputation)
client.close()
AbuseIPDB (IP addresses):
import requests
response = requests.get(
"https://api.abuseipdb.com/api/v2/check",
headers={"Key": "YOUR_KEY", "Accept": "application/json"},
params={"ipAddress": "1.2.3.4", "maxAgeInDays": 90}
)
data = response.json()["data"]
print(f"Confidence: {data['abuseConfidenceScore']}%, Reports: {data['totalReports']}")
MalwareBazaar (file hashes):
response = requests.post(
"https://mb-api.abuse.ch/api/v1/",
data={"query": "get_info", "hash": sha256_hash}
)
result = response.json()
if result["query_status"] == "ok":
print(result["data"][0]["tags"], result["data"][0]["signature"])
Query MISP for existing events matching the IOC:
from pymisp import PyMISP
misp = PyMISP("https://misp.example.com", "API_KEY")
results = misp.search(value="evil-domain.com", type_attribute="domain")
for event in results:
print(event["Event"]["info"], event["Event"]["threat_level_id"])
Check Shodan for IP context (hosting provider, open ports, banners) to identify if the IP belongs to bulletproof hosting or a legitimate cloud provider (false positive risk).
Apply a tiered decision framework:
Record findings in TIP/MISP with:
Export to STIX indicator object with confidence field set appropriately.
| Term | Definition |
|------|-----------|
| IOC | Indicator of Compromise — observable network or host artifact indicating potential compromise |
| Enrichment | Process of adding contextual data to a raw IOC from multiple intelligence sources |
| Defanging | Modifying IOCs (replacing . with [.]) to prevent accidental activation in documentation |
| False Positive Rate | Percentage of benign artifacts incorrectly flagged as malicious; critical for tuning block thresholds |
| Sinkhole | DNS server redirecting malicious domain lookups to a benign IP for detection without blocking traffic entirely |
| TTL | Time-to-live for an IOC in blocking controls; IP indicators should expire after 30 days, domains after 90 days |
development
Detect Pass-the-Hash (T1550.002) attacks by analyzing NTLM authentication patterns, flagging Type 3 logons using NTLM where Kerberos would be expected, and correlating with credential-dumping indicators. Use when threat hunting for lateral movement via stolen NTLM hashes, triaging EDR/SIEM alerts on suspicious NTLM logons, scoping compromise during incident response, or validating detection coverage in a purple team exercise.
testing
Detect and respond to OAuth token theft and replay in Microsoft Entra ID (Azure AD), covering access token theft, refresh token replay, Primary Refresh Token (PRT) abuse, pass-the-cookie attacks, and Token Protection conditional access policies. Use for impossible-travel or anomalous token-usage alerts, suspected session hijacking, sign-in log analysis, or configuring token-binding defenses in Azure/M365.
development
Detect NTLM relay attacks (T1557.001) by correlating Windows Event 4624 LogonType 3 for IP-to-hostname mismatches, identifying Responder/LLMNR poisoning artifacts, auditing SMB/LDAP signing, and flagging NTLMv2-to-NTLMv1 downgrades. Use for hunting credential relay in NTLM-enabled AD, investigating auth-source anomalies, building SIEM correlation rules, or responding to PetitPotam/DFSCoerce/PrinterBug alerts.
data-ai
Detect network reconnaissance and port scanning using Suricata and Snort IDS signatures, threshold-based detection rules, and traffic anomaly analysis to identify Nmap, Masscan, and custom scanning activity.